scholarly journals Evidence for Multiple Teosinte Hybrid Zones in Central Mexico

2021 ◽  
Author(s):  
David E. Hufnagel ◽  
Kathryn Kananen ◽  
Jeffrey C. Glaubitz ◽  
José de Jesuś Sánchez-González ◽  
John F. Doebley ◽  
...  
Keyword(s):  
Zea Mays ◽  
Isolation By Distance ◽  
Central Mexico ◽  
Hybrid Zones ◽  
List Type ◽  
Data Set ◽  
Snp Data ◽  
Locally Adaptive ◽  
Range Overlap ◽  
Adaptation Variable

1SummaryHybrid zones provide an excellent opportunity for studying population dynamics and whether hybrid genetic architectures are locally adaptive. The genus Zea contains many diverse wild taxa collectively called teosinte. Zea mays ssp. parviglumis, the lowland progenitor of maize (Zea mays ssp. mays), and its highland relative Zea mays ssp. mexicana live parapatrically and, while putative hybrids have been identified in regions of range overlap, these have never been deeply explored.Here we use a broadly sampled SNP data set to identify and confirm 112 hybrids between Zea mays ssp. parviglumis and Zea mays ssp. mexicana, mostly clustered in three genetically and geographically distinct hybrid groups in Central Mexico.These hybrid groups inhabit intermediate environments relative to parental taxa. We demonstrate that these individuals are true hybrids and not products of isolation by distance or ancestral to parviglumis and mexicana. This work expands on previous studies, clearly identifying hybrid zones in Zea, genetically characterizing hybrid groups, and showing what appear to be unique genetic architectures of hybridization in distinct hybrid groups.With the potential for local adaptation, variable hybrid zone dynamics, and differential architectures of hybridization, we present these teosinte hybrids and parental taxa as a promising model system for studying hybridization and hybrid zones.

Population Subdivision and Molecular Sequence Variation: Theory and Analysis of Drosophila ananassae Data

Genetics ◽  
2003 ◽  
Vol 165 (3) ◽  
pp. 1385-1395
Author(s):  
Claus Vogl ◽  
Aparup Das ◽  
Mark Beaumont ◽  
Sujata Mohanty ◽  
Wolfgang Stephan
Keyword(s):  
Sequence Data ◽  
Isolation By Distance ◽  
Allele Frequencies ◽  
Drosophila Ananassae ◽  
Population Subdivision ◽  
Variation Theory ◽  
Peripheral Populations ◽  
Molecular Variation ◽  
Data Set ◽  
Evolutionary Forces

Abstract Population subdivision complicates analysis of molecular variation. Even if neutrality is assumed, three evolutionary forces need to be considered: migration, mutation, and drift. Simplification can be achieved by assuming that the process of migration among and drift within subpopulations is occurring fast compared to mutation and drift in the entire population. This allows a two-step approach in the analysis: (i) analysis of population subdivision and (ii) analysis of molecular variation in the migrant pool. We model population subdivision using an infinite island model, where we allow the migration/drift parameter 0398; to vary among populations. Thus, central and peripheral populations can be differentiated. For inference of 0398;, we use a coalescence approach, implemented via a Markov chain Monte Carlo (MCMC) integration method that allows estimation of allele frequencies in the migrant pool. The second step of this approach (analysis of molecular variation in the migrant pool) uses the estimated allele frequencies in the migrant pool for the study of molecular variation. We apply this method to a Drosophila ananassae sequence data set. We find little indication of isolation by distance, but large differences in the migration parameter among populations. The population as a whole seems to be expanding. A population from Bogor (Java, Indonesia) shows the highest variation and seems closest to the species center.

scholarly journals Bayesian Inference of Species Trees using Diffusion Models

2020 ◽  
Vol 70 (1) ◽  
pp. 145-161 ◽  
Author(s):  
Marnus Stoltz ◽  
Boris Baeumer ◽  
Remco Bouckaert ◽  
Colin Fox ◽  
Gordon Hiscott ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Numerical Algorithms ◽  
Diffusion Models ◽  
Model Parameters ◽  
Data Sets ◽  
Species Trees ◽  
Computationally Efficient ◽  
Data Set ◽  
Snp Data ◽  
Binary Markers

Abstract We describe a new and computationally efficient Bayesian methodology for inferring species trees and demographics from unlinked binary markers. Likelihood calculations are carried out using diffusion models of allele frequency dynamics combined with novel numerical algorithms. The diffusion approach allows for analysis of data sets containing hundreds or thousands of individuals. The method, which we call Snapper, has been implemented as part of the BEAST2 package. We conducted simulation experiments to assess numerical error, computational requirements, and accuracy recovering known model parameters. A reanalysis of soybean SNP data demonstrates that the models implemented in Snapp and Snapper can be difficult to distinguish in practice, a characteristic which we tested with further simulations. We demonstrate the scale of analysis possible using a SNP data set sampled from 399 fresh water turtles in 41 populations. [Bayesian inference; diffusion models; multi-species coalescent; SNP data; species trees; spectral methods.]

scholarly journals Comparative population genetic structure of two Ixodidae ticks (Ixodes ovatus and Haemaphysalis flava) in Niigata Prefecture, Japan

2019 ◽  
Author(s):  
Maria Angenica Fulo Regilme ◽  
Megumi Sato ◽  
Tsutomu Tamura ◽  
Reiko Arai ◽  
Marcello Otake Sato ◽  
...  
Keyword(s):  
Genetic Structure ◽  
Large Scale ◽  
Southern China ◽  
Isolation By Distance ◽  
Mantel Test ◽  
Rrna Gene ◽  
Data Set ◽  
Genetic Groups ◽  
Niigata Prefecture ◽  
Haemaphysalis Flava

AbstractIxodid tick species such as Ixodes ovatus and Haemaphysalis flava are important vector of tick-borne diseases in Japan. In this study, we used genetic structure at two mitochondrial loci (cox1, 16S rRNA gene) to infer gene flow patterns of I. ovatus and H. flava from Niigata Prefecture, Japan. Samples were collected in 29 (I. ovatus) and 17 (H. flava) sampling locations across Niigata Prefecture (12,584.18 km2). For I. ovatus, pairwise FST and analysis of molecular variance (AMOVA) analyses of cox1 sequences indicated significant among-population differentiation. This was in contrast to H. flava, for which there were few cases of low significant pairwise differentiation. A Mantel test revealed isolation by distance and there was positive spatial autocorrelation of haplotypes in I. ovatus cox1 and 16S sequences, but non-significant results were observed in H. flava in both markers. We found three genetic groups (China 1, China 2 and Japan) in the cox1 I. ovatus tree. Newly sampled I. ovatus grouped together with a published I. ovatus sequence from northern Japan and were distinct from two other I. ovatus groups that were reported from southern China. The three genetic groups in our data set suggest the potential for cryptic species among the groups. While many factors can potentially account for the observed differences in genetic structure between the two species, including population persistence and large-scale patterns of range expansion, the differences in the mobility of hosts of tick immature stages (small mammals in I. ovatus; birds in H. flava) is possibly driving the observed patterns.

scholarly journals Reducing bias in population and landscape genetic inferences: the effects of sampling related individuals and multiple life stages

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e1813 ◽  
Author(s):  
William Peterman ◽  
Emily R. Brocato ◽  
Raymond D. Semlitsch ◽  
Lori S. Eggert
Keyword(s):  
Best Practice ◽  
Isolation By Distance ◽  
Life Stage ◽  
Population Sample ◽  
Life Stages ◽  
Effective Population ◽  
Data Set ◽  
Sibling Pairs ◽  
Population Sizes ◽  
Landscape Genetic

In population or landscape genetics studies, an unbiased sampling scheme is essential for generating accurate results, but logistics may lead to deviations from the sample design. Such deviations may come in the form of sampling multiple life stages. Presently, it is largely unknown what effect sampling different life stages can have on population or landscape genetic inference, or how mixing life stages can affect the parameters being measured. Additionally, the removal of siblings from a data set is considered best-practice, but direct comparisons of inferences made with and without siblings are limited. In this study, we sampled embryos, larvae, and adultAmbystoma maculatumfrom five ponds in Missouri, and analyzed them at 15 microsatellite loci. We calculated allelic richness, heterozygosity and effective population sizes for each life stage at each pond and tested for genetic differentiation (FSTandDC) and isolation-by-distance (IBD) among ponds. We tested for differences in each of these measures between life stages, and in a pooled population of all life stages. All calculations were done with and without sibling pairs to assess the effect of sibling removal. We also assessed the effect of reducing the number of microsatellites used to make inference. No statistically significant differences were found among ponds or life stages for any of the population genetic measures, but patterns of IBD differed among life stages. There was significant IBD when using adult samples, but tests using embryos, larvae, or a combination of the three life stages were not significant. We found that increasing the ratio of larval or embryo samples in the analysis of genetic distance weakened the IBD relationship, and when usingDC, the IBD was no longer significant when larvae and embryos exceeded 60% of the population sample. Further, power to detect an IBD relationship was reduced when fewer microsatellites were used in the analysis.

scholarly journals Culture and therapist self-disclosure

2019 ◽  
Vol 12 ◽  
Author(s):  
Peter Phiri ◽  
Shanaya Rathod ◽  
Mary Gobbi ◽  
Hannah Carr ◽  
David Kingdon
Keyword(s):  
Drop Out ◽  
List Type ◽  
Behaviour Therapy ◽  
Data Set ◽  
Self Disclosure ◽  
Psychological Therapies ◽  
Cognitive Behaviour ◽  
Lay Members ◽  
Poor Outcomes ◽  
The Impact

AbstractCognitive behaviour therapy (CBT) as a treatment for schizophrenia and psychotic-related disorders has been shown to have significantly greater drop-out rates in clients of black and minority ethnic (BME) groups. This has resulted in poor outcomes in treatments. Our recent qualitative study thus aimed to develop culturally sensitive CBT for BME clients. The study consisted of individual in-depth 1:1 interviews with patients with a diagnosis of schizophrenia, schizo-affective, delusional disorders or psychosis (n = 15) and focus groups with lay members (n = 52), CBT therapists (n = 22) and mental health practitioners (n = 25) on a data set of 114 participants. Several themes emerged relating to therapist awareness on culturally derived behaviours, beliefs and attitudes that can influence client response and participation in therapy. The current paper aims to explore one of these themes in greater detail, i.e. client-initiated therapist self-disclosure (TSD). Using thematic analysis, the paper highlights key elements of TSD and how this could impact on therapist’s reactions towards TSD, the therapeutic alliance and ultimately, the outcomes of therapy. The findings appear to show that TSD has significant relevance in psychological practice today. Some BME client groups appear to test therapists through initiating TSD. It is not the content of TSD they are testing per se, but how the therapist responds. Consequently, this requires therapists’ cognisance and sensitive responses in a manner that will nurture trust and promote rapport. Further investigation in this area is suggested with a recommendation for guidelines to be created for clinicians and training.Key learning aims(1)To develop a dialogue and practice with confidence when addressing issues of self-disclosure with diverse populations.(2)To appreciate the impact therapist self-disclosure has in early stages of engagement, in particular when working with patients from BME communities.(3)To understand the impact and role of self-disclosure as initiated by patients.(4)To increase therapist awareness on cultural differences in self-disclosure and develop ways to address this in therapy.(5)To challenge therapists to adapt psychological therapies to diverse cultures and be cognisant that ‘one size does not fit all’.

scholarly journals Enhancing 15N-uptake in maize (Zea mays L.) by native Trichoderma spp. strains in Central Mexico

2019 ◽  
Vol 18 (22) ◽  
pp. 478-488
Author(s):  
J. A. Vera-Nunez ◽  
F. Luna-Martínez ◽  
M. S. Barcos-Arias ◽  
M. E. Avila-Miranda ◽  
O. A. Grageda-Cabrera ◽  
...  
Keyword(s):  
Zea Mays ◽  
Zea Mays L ◽  
Central Mexico ◽  
Trichoderma Spp ◽  
15N Uptake

scholarly journals Empirical Bayesian approach to testing multiple hypotheses with separate priors for left and right alternatives

2018 ◽  
Vol 17 (4) ◽  
Author(s):  
Naveen K. Bansal ◽  
Mehdi Maadooliat ◽  
Steven J. Schrodi
Keyword(s):  
Empirical Bayes ◽  
Bayes Rule ◽  
Test Statistic ◽  
Data Set ◽  
Web Based ◽  
Multiple Hypotheses ◽  
Snp Data ◽  
Genome Wide ◽  
Shiny App ◽  
User Friendly

Abstract We consider a multiple hypotheses problem with directional alternatives in a decision theoretic framework. We obtain an empirical Bayes rule subject to a constraint on mixed directional false discovery rate (mdFDR≤α) under the semiparametric setting where the distribution of the test statistic is parametric, but the prior distribution is nonparametric. We proposed separate priors for the left tail and right tail alternatives as it may be required for many applications. The proposed Bayes rule is compared through simulation against rules proposed by Benjamini and Yekutieli and Efron. We illustrate the proposed methodology for two sets of data from biological experiments: HIV-transfected cell-line mRNA expression data, and a quantitative trait genome-wide SNP data set. We have developed a user-friendly web-based shiny App for the proposed method which is available through URL https://npseb.shinyapps.io/npseb/. The HIV and SNP data can be directly accessed, and the results presented in this paper can be executed.

Comparison of Object Detection Methods for Corn Damage Assessment Using Deep Learning

2020 ◽  
Vol 63 (6) ◽  
pp. 1969-1980
Author(s):  
Ali Hamidisepehr ◽  
Seyed V. Mirnezami ◽  
Jason K. Ward
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Object Detection ◽  
Damage Detection ◽  
Damage Identification ◽  
Severe Weather ◽  
Crop Damage ◽  
Growth Stages ◽  
List Type ◽  
Data Set

HighlightsCorn damage detection was possible using advanced deep learning and computer vision techniques trained with images of simulated corn lodging.RetinaNet and YOLOv2 both worked well at identifying regions of lodged corn.Automating crop damage identification could provide useful information to producers and other stakeholders from visual-band UAS imagery.Abstract. Severe weather events can cause large financial losses to farmers. Detailed information on the location and severity of damage will assist farmers, insurance companies, and disaster response agencies in making wise post-damage decisions. The goal of this study was a proof-of-concept to detect areas of damaged corn from aerial imagery using computer vision and deep learning techniques. A specific objective was to compare existing object detection algorithms to determine which is best suited for corn damage detection. Simulated corn lodging was used to create a training and analysis data set. An unmanned aerial system equipped with an RGB camera was used for image acquisition. Three popular object detectors (Faster R-CNN, YOLOv2, and RetinaNet) were assessed for their ability to detect damaged areas. Average precision (AP) was used to compare object detectors. RetinaNet and YOLOv2 demonstrated robust capability for corn damage identification, with AP ranging from 98.43% to 73.24% and from 97.0% to 55.99%, respectively, across all conditions. Faster R-CNN did not perform as well as the other two models, with AP between 77.29% and 14.47% for all conditions. Detecting corn damage at later growth stages was more difficult for all three object detectors. Keywords: Computer vision, Faster R-CNN, RetinaNet, Severe weather, Smart farming, YOLO.

scholarly journals Gene regulatory effects of a large chromosomal inversion in highland maize

2019 ◽  
Author(s):  
Taylor Crow ◽  
James Ta ◽  
Saghi Nojoomi ◽  
M. Rocío Aguilar-Rangel ◽  
Jorge Vladimir Torres Rodríguez ◽  
...  
Keyword(s):  
Gene Expression ◽  
Zea Mays ◽  
Large Scale ◽  
Chromosomal Inversion ◽  
Experimental Conditions ◽  
Chromosomal Inversions ◽  
Functional Variants ◽  
Original Manuscript ◽  
Locally Adaptive ◽  
Highland Maize

AbstractChromosomal inversions play an important role in local adaptation. Inversions can capture multiple locally adaptive functional variants in a linked block by repressing recombination. However, this recombination suppression makes it difficult to identify the genetic mechanisms that underlie an inversion’s role in adaption. In this study, we explore how large-scale transcriptomic data can be used to dissect the functional importance of a 13 Mb inversion locus (Inv4m) found almost exclusively in highland populations of maize (Zea mays ssp. mays). Inv4m introgressed into highland maize from the wild relative Zea mays ssp. mexicana, also present in the highlands of Mexico, and is thought to be important for the adaptation of these populations to cultivation in highland environments. First, using a large publicly available association mapping panel, we confirmed that Inv4m is associated with locally adaptive agronomic phenotypes, but only in highland fields. Second, we created two families segregating for standard and inverted haplotypess of Inv4m in a isogenic B73 background, and measured gene expression variation association with Inv4m across 9 tissues in two experimental conditions. With these data, we quantified both the global transcriptomic effects of the highland Inv4m haplotype, and the local cis-regulatory variation present within the locus. We found diverse physiological effects of Inv4m, and speculate that the genetic basis of its effects on adaptive traits is distributed across many separate functional variants.Author SummaryChromosomal inversions are an important type of genomic structural variant. However, mapping causal alleles within their boundaries is difficult because inversions suppress recombination between homologous chromosomes. This means that inversions, regardless of their size, are inherited as a unit. We leveraged the high-dimensional phenotype of gene expression as a tool to study the genetics of a large chromosomal inversion found in highland maize populations in Mexico - Inv4m. We grew plants carrying multiple versions of Inv4m in a common genetic background, and quantified the transcriptional reprogramming induced by alternative alleles at the locus. Inv4m has been shown in previous studies to have a large effect on flowering, but we show that the functional variation within Inv4m affects many developmental and physiological processes.Author ContributionsT. Crow, R. Rellan-Alvarez, R. Sawers and D. Runcie conceived and designed the experiment. M. Aguilar-Rangel, J. Rodrǵuez, R. Rellan-Alvarez and R. Sawers generated the segregating families. T. Crow, J. Ta, S. Nojoomi, M. Aguilar-Rangel, J. Rodrǵuez D. Gates, D. Runcie performed the experiment. T. Crow, D. Gates, D. Runcie analyzed the data. T. Crow, D. Runcie wrote the original manuscript, and R. Rellan-Alvarez and R. Sawers provided review and editing.

scholarly journals Calculating effect sizes in animal social network analysis

2020 ◽  
Author(s):  
Daniel W. Franks ◽  
Michael N. Weiss ◽  
Matthew J. Silk ◽  
Robert J. Y. Perryman ◽  
Darren. P. Croft
Keyword(s):  
Social Network ◽  
Effect Size ◽  
Social Preferences ◽  
Effect Sizes ◽  
Network Data ◽  
List Type ◽  
Data Set ◽  
Social Network Data ◽  
Confounding Variables ◽  
Nodal Model

AbstractBecause of the nature of social interaction or association data, when testing hypotheses using social network data it is common for network studies to rely on permutations to control for confounding variables, and to not also control for them in the fitted statistical model. This can be a problem because it does not adjust for any bias in effect sizes generated by these confounding effects, and thus the effect sizes are not informative in the presence of counfouding variables.We implemented two network simulation examples and analysed an empirical data set to demonstrate how relying solely on permutations to control for confounding variables can result in highly biased effect size estimates of animal social preferences that are uninformative when quantifying differences in behaviour.Using these simulations, we show that this can sometimes even lead to effect sizes that have the wrong sign and are thus the effect size is not biologically interpretable. We demonstrate how this problem can be addressed by controlling for confounding variables in the statistical dyadic or nodal model.We recommend this approach should be adopted as standard practice in the statistical analysis of animal social network data.

Sign in / Sign up

Export Citation Format

Share Document